Pablo is available for hire
Hire PabloPablo Hernandez Cerdan
Verified Expert in Engineering
Software Engineer and Developer
Location
Torre-Pacheco, Spain
Toptal Member Since
April 7, 2022
Pablo holds a Ph.D. in physics and is a top software engineer contributing to open-source libraries in image analysis and visualization. He loves the thrill of creating inspiring projects with his software skills. Pablo is proficient in C++, Python, and CMake. He can be found maintaining the release cycle of an open-source library in C++ with Python bindings or using PyTorch to create industry-level AI solutions in computer vision. He is also a contributor to a Web3 open-science journal.
Portfolio
Innolitics, LLC
Python, C++, 3D Image Processing, Supervised Machine Learning, Generative AI
Mundohuevo
Deep Learning, PyTorch, Amazon Web Services (AWS), Python...
AANappDay DMCC
Artificial Intelligence (AI), Python, C++, Deep Learning, Qt, OpenMP, CMake
Experience
Availability
Part-time
Preferred Environment
Linux, Vim Text Editor, Tmux
The most amazing...
...project I've developed is an AI solution for a health startup for automatic segmentation of vasculature from medical images, including data curation.
Work Experience
Software Engineer
2023 - PRESENT
Innolitics, LLC
- Developed an app from end-to-end, later approved by the FDA, to detect enlarged hearts from CT images.
- Architected tools to handle and curate annotated data.
- Created automatic deployment scripts to AWS ECR using Docker.
Technologies: Python, C++, 3D Image Processing, Supervised Machine Learning, Generative AI
Senior Deep Learning Engineer
2022 - 2023
Mundohuevo
- Developed a generative adversarial network to create images of human irises. Trained an image translation neural network to generate photo-realistic results from low-resolution images sent by clients.
- Supervised acquisition, curation, and preprocessing of the custom, private dataset. Mentored image technicians.
- Performed training on the generative model on AWS.
Technologies: Deep Learning, PyTorch, Amazon Web Services (AWS), Python, Generative Adversarial Networks (GANs), Minimum Viable Product (MVP), Google Cloud
Deep Learning Analyst
2022 - 2022
AANappDay DMCC
- Developed core algorithms to solve imperfect information games. This included research of the game theory and machine learning community and its implementation in the C++ codebase with Python bindings.
- Performed the architectural reorganization of the whole codebase, adding CMake files to manage compilation and testing for multiplatform. Also, automatized it via CMake the injection of third-parties dependencies.
- Added CI/CD pipelines with GitHub Actions to continuously test it in Windows and Linux.
- Tracked and fixed bugs using Jira and Github platforms.
- Modernized a legacy codebase with best C++ practices. Detecting bugs and increasing the correctness of algorithms.
Technologies: Artificial Intelligence (AI), Python, C++, Deep Learning, Qt, OpenMP, CMake
Senior Image Processing and 3D Modeling Engineer
2022 - 2022
UNIFi3D (Hong Kong) Limited
- Explored the research space for alternatives and identified the best solution for the client.
- Implemented algorithmic solutions based on texture manipulation.
- Implemented a deep learning (GAN) solution tailored to the client's needs.
Technologies: Python, Machine Learning, Image Processing, Deep Learning, Research, Artificial Intelligence (AI), Algorithms, Generative Models, OpenCV, Minimum Viable Product (MVP), Proof of Concept (POC)
Senior AI Engineer
2021 - 2022
Cella Medical Solutions
- Developed an AI solution for automatic segmentation of vasculature from CT images.
- Curated and organized the company datasets. Created a script to extract images with certain features for training purposes.
- Mentored co-workers on image analysis best practices, such as keeping the geometry metadata in sync with the data augmentation transformations.
Technologies: Python, Artificial Intelligence (AI), PyTorch, Computer Vision, Deep Learning, Image Analysis, Neural Networks, Machine Learning, Scikit-image, Scikit-learn, Image Processing, NumPy, Data Engineering, 3D Image Processing, Convolutional Neural Networks (CNN), Machine Learning Operations (MLOps), Leadership, Object Detection, Google Cloud
Research and Development Engineer
2021 - 2021
Medizinische Hochschule Hannover
- Developed a watershed on meshes algorithm to segment alveoli in high resolution pulmonary images.
- Provided Python bindings to the high performance C++ code.
- Mentored research students on how to use it, providing extensive documentation and tests.
Technologies: C++, Python, PyBind11, Image Analysis, Computer Vision, Algorithms, Mathematical Modeling, Graph Theory, 3D Image Processing, Minimum Viable Product (MVP)
Senior Data Specialist
2021 - 2021
NumFOCUS, Inc.
- Received a NumFOCUS small development grant to modernize the Insight Journal.
- Provided DOI's to every article ever submitted to the Insight Journal through Crossref's API.
- Uploaded all data to the Interplanetary File System (IPFS), to meet the Web 3.0 and keep all the data reachable for reproducibility purposes.
Technologies: PostgreSQL, Python, SQL, Algorithms
Research and Development Engineer
2020 - 2020
Medizinische Hochschule Hannover
- Developed an SGGEN algorithm to study generations (branching) on vascular trees from pulmonary 3D images.
- Integrated into an open-source library SGEXT as an external module. Added Python bindings, testing, and CI/CD pipelines.
- Tested real images and published a research article about it.
Technologies: C++, Python, CMake, Computer Vision, Open Source, Image Analysis, Algorithms, Mathematical Modeling, 3D Image Processing
Research and Development Engineer
2020 - 2020
CNRS
- Provided Python bindings to the digital geometry tools and algorithms Library (DGtal), a big C++ library on digital topology using PyBind11.
- Provided extensive documentation and test frameworks for other developers to add more functionality.
- Created CI/CD pipelines from scratch using Azure pipelines.
Technologies: C++, Python, PyBind11, DGtal, Open Source, CI/CD Pipelines, NumPy
Research and Development Engineer
2019 - 2020
MacDiarmid Institute
- Developed algorithms for simulating in-silico biopolymer networks for reservoir computing (AI). The algorithms included dynamics of single polymers and force propagation on the system.
- Created Python bindings of the high performance C++ code for easy adoption.
- Created the open-source SGEXT library. Tested and documented the work, including regular releases to PyPI.
Technologies: C++, Python, CMake, Boost, Graphs, Artificial Intelligence (AI), SGEXT, Open Source, Docker, Algorithms, Mathematical Modeling, Physics, Physics Simulations
Research and Development Intern
2018 - 2019
Kitware
- Applied Wavelets algorithms to detect cracks on teeth from medical images.
- Created a custom Slicer3D application: SlicerSALT to bundle important algorithms on shape analysis research.
- Mentored by top engineers in the field of medical analysis and learned best practices on software development from industry experts.
Technologies: C++, CMake, Research, Wavelets, SlicerSALT, Open Source, Image Analysis, Algorithms, Machine Learning, Scikit-learn, Fourier Analysis, Mathematical Modeling, Physics, CI/CD Pipelines, Image Processing, Point Clouds, 3D Image Processing, Qt, OpenCV, Object Detection
Experience
SGEXT
SGEXT is an acronym for Spatial Graph Extractor. It is a C++ library with Python bindings based on my ongoing soft-matter and medical imaging research.
The library's main purpose is to skeletonize tubular structures from input images and extract a graph representation from them. This allows characterizing the objects of interest in a simplified yet insightful way. It also provides simulation of polymer networks as spring networks and single-chain dynamics using Monte Carlo methods. The library has been used to study the statistical properties of polymeric materials and extract graphs from vasculature from medical images (airways). It will also be used to study Reservoir Computing (AI) in in-silico polymer networks.
I was the sole developer of this project. It's fully tested, documented, and with CI/CD pipelines. It provides Python bindings using PyBind11 for an easier interface in Python for researchers. The package is uploaded to PyPI regularly.
The library's main purpose is to skeletonize tubular structures from input images and extract a graph representation from them. This allows characterizing the objects of interest in a simplified yet insightful way. It also provides simulation of polymer networks as spring networks and single-chain dynamics using Monte Carlo methods. The library has been used to study the statistical properties of polymeric materials and extract graphs from vasculature from medical images (airways). It will also be used to study Reservoir Computing (AI) in in-silico polymer networks.
I was the sole developer of this project. It's fully tested, documented, and with CI/CD pipelines. It provides Python bindings using PyBind11 for an easier interface in Python for researchers. The package is uploaded to PyPI regularly.
Insight Journal
https://www.insight-journal.org/A revamp to a historical, scientific journal in image analysis, based on GatsbyJS, and the key feature of providing each article with a Digital Object Identifier (DOI) through Crossref.
The DOI provides authors extra pay for their work because they can be parsed by Web of Science, Google Scholar, etc., boosting their research statistics.
My work focused on the back-end, cleaning and curating the original database and creating a new static and open database based on GraphQL. I created automated scripts to register the current articles to Crossref through their API. The PDFs and source code of the publications were uploaded to the IPFS network. This was the first scientific journal using Web3.0 tools.
The DOI provides authors extra pay for their work because they can be parsed by Web of Science, Google Scholar, etc., boosting their research statistics.
My work focused on the back-end, cleaning and curating the original database and creating a new static and open database based on GraphQL. I created automated scripts to register the current articles to Crossref through their API. The PDFs and source code of the publications were uploaded to the IPFS network. This was the first scientific journal using Web3.0 tools.
Vasomaly
https://vasomaly.com/A personal project to study anomalies in vasculature and airways from medical images. It is based on my research on the topic of spatial graphs and uses graphs and machine learning to localize anomalies, such as aneurysms, or arteriovenous malformations. The static webpage was developed using GatsbyJS and hosted in Google.
Skillset
Languages
C++, Python, SQL, GraphQL, XML, Fortran
Libraries/APIs
PyTorch, VTK, NetworkX, NumPy, Scikit-learn, OpenCV, OpenGL, OpenMP
Tools
Vim Text Editor, Git, CMake, ITK, Tmux, Scikit-image, Jekyll
Platforms
Linux, Azure, Docker, Amazon Web Services (AWS)
Other
Critical Thinking, PyBind11, Artificial Intelligence (AI), Image Analysis, Graphs, Research, Medical Imaging, Computer Vision, Deep Learning, Open Source, Neural Networks, Algorithms, Machine Learning, Fourier Analysis, Mathematical Modeling, Physics, Simulated Annealing, Physics Simulations, CI/CD Pipelines, Image Processing, Graph Theory, 3D Image Processing, Minimum Viable Product (MVP), Biotechnology, Technical Writing, Monte Carlo Simulations, Data Engineering, Convolutional Neural Networks (CNN), Signal Processing, Generative Models, Object Detection, Proof of Concept (POC), Production, APIs, IPFS, Gatsby, Google, DGtal, SGEXT, SlicerSALT, Point Clouds, Machine Learning Operations (MLOps), Leadership, Generative Adversarial Networks (GANs), Supervised Machine Learning, Generative AI
Frameworks
Boost, Qt, Google Test
Paradigms
Wavelets
Storage
PostgreSQL, Google Cloud
Education
2013 - 2018
Ph.D. in Biophysics
Massey University - Palmerston North, New Zealand
2010 - 2012
Master's Degree in Biophysics
Universidad Autonoma de Madrid - Madrid, Spain
2004 - 2010
Bachelor's Degree in Physics
Universidad de Murcia - Murcia, Spain
Certifications
OCTOBER 2021 - PRESENT
Introduction to Machine Learning in Production
Coursera
OCTOBER 2021 - PRESENT
Deep Neural Networks with PyTorch
Coursera
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